Spatial assessment of the effects of in situ and neighbourhood factors on urban land surface temperature mitigation in a rapidly developing region

The surface energy balance (SEB) is defined as the balance between incoming energy from the sun and outgoing energy from the Earth&#8217;s surface. All components of the SEB depend on land surface temperature (LST). Therefore, LST is an important state variable that controls the energy and water exchange between the Earth&#8217;s surface and the atmosphere. LST can be estimated radiometrically, based on the infrared radiance emanating from the surface. At the landscape scale, LST is derived from thermal radiation measured using&#160; satellites.&#160; At the plot scale, eddy covariance flux towers commonly record downwelling and upwelling longwave radiation, which can be inverted to retrieve LST&#160; using the grey body equation : &#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160; Rlup = &#949;&#963; Ts4 + (1 &#8722; &#949;) R ldw&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160; (1) where Rlup is the upwelling longwave radiation, Rldw is the downwelling longwave radiation, &#949; is the surface emissivity, Ts&#160; is the surface temperature and &#963;&#160; is the Stefan-Boltzmann constant. The first term is the temperature-dependent part, while the second represents reflected longwave radiation. Since in the past downwelling longwave radiation was not measured routinely using flux towers, it is an established practice to only use upwelling longwave radiation for the retrieval of plot-scale LST, essentially neglecting the reflected part and shortening Eq. 1 to: &#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160; Rlup = &#949;&#963; Ts4 &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160;&#160; (2) Despite&#160; widespread availability of downwelling longwave radiation measurements, it is still common to use the short equation (Eq. 2) for in-situ LST retrieval. This prompts the question if ignoring the downwelling longwave radiation introduces a bias in LST estimations from tower measurements. Another associated question is how to obtain the correct &#949; needed for in-situ LST retrievals using tower-based measurements. The current work addresses these two important science questions using observed fluxes at eddy covariance towers for different land cover types. Additionally, uncertainty in retrieved LST and emissivity due to uncertainty in input fluxes was quantified using SOBOL-based uncertainty analysis (SALib). Using landscape-scale emissivity obtained from satellite data (MODIS), we found that the LST&#160; obtained using the complete equation (Eq. 1) is 0.5 to 1.5 K lower than the short equation (Eq. 2). Also, plot-scale emissivity was estimated using observed sensible heat flux and surface-air temperature differences. Plot-scale emissivity obtained using the complete equation was generally between 0.8 to 0.98 while the short equation gave values between 0.9 to 0.98, for all land cover types. Despite additional input data for the complete equation, the uncertainty in plot-scale LST was not greater than if the short equation was used. Landscape-scale daytime LST obtained from satellite data (MODIS TERRA) were strongly correlated with our plot-scale estimates, but on average higher by 0.5 to 9 K, regardless of the equation used. However, for most sites, the correspondence between MODIS TERRA LST and retrieved plot-scale LST estimates increased significantly if plot-scale emissivity was used instead of the landscape-scale emissivity obtained from satellite data.

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A comparison of MODIS land surface temperature with in situ observations

Geophysical Research Letters ◽

10.1029/2006gl027519 ◽

2006 ◽

Vol 33 (20) ◽

Cited By ~ 27

Author(s):

Michael G. Bosilovich

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Modis Land Surface Temperature ◽

In Situ Observations

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Variational Assimilation of Land Surface Temperature within the ORCHIDEE Land Surface Model Version 1.2.6

10.5194/gmd-2016-64 ◽

2016 ◽

Author(s):

H. S. Benavides Pinjosovsky ◽

S. Thiria ◽

C. Ottlé ◽

J. Brajard ◽

F. Badran ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Initial Conditions ◽

Land Surface Model ◽

Surface Model ◽

Adjoint Model ◽

Variational Assimilation ◽

Internal Parameters

Abstract. The SECHIBA module of the ORCHIDEE land surface model describes the exchanges of water and energy between the surface and the atmosphere. In the present paper, the adjoint semi-generator software denoted YAO was used as a framework to implement a 4D-VAR assimilation method. The objective was to deliver the adjoint model of SECHIBA (SECHIBA-YAO) obtained with YAO to provide an opportunity for scientists and end users to perform their own assimilation. SECHIBA-YAO allows the control of the eleven most influent internal parameters of SECHIBA or of the initial conditions of the soil water content by observing the land surface temperature measured in situ or as it could be observed by remote sensing as brightness temperature. The paper presents the fundamental principles of the 4D-Var assimilation, the semi-generator software YAO and some experiments showing the accuracy of the adjoint code distributed. In addition, a distributed version is available when only the land surface temperature is observed.

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Development of Downscaled Urban Land Surface Temperature for New York City

10.1109/igarss47720.2021.9553201 ◽

2021 ◽

Author(s):

Abdou Bah ◽

Hamid Norouzi ◽

Satya Prakash ◽

Makini Valentine ◽

Reginald Blake

Keyword(s):

New York ◽

New York City ◽

Surface Temperature ◽

Land Surface Temperature ◽

York City ◽

Land Surface ◽

Urban Land

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Evaluating the potential of MODIS-LST for monitoring ground surface temperatures in the Maritime Antarctic (Barton Peninsula, King George Island, Antarctic).

10.5194/egusphere-egu21-10415 ◽

2021 ◽

Author(s):

Alejandro Corbea-Pérez ◽

Gonçalo Vieira ◽

Carmen Recondo ◽

Joana Baptista ◽

Javier F.Calleja ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Ground Temperature ◽

King George Island ◽

Maritime Antarctic ◽

In Situ Data ◽

Surface Temperatures ◽

Barton Peninsula

Land surface temperature is an important factor for permafrost modelling as well as for understanding the dynamics of Antarctic terrestrial ecosystems (Bockheim et al. 2008). In the South Shetland Islands the distribution of permafrost is complex (Vieira et al. 2010) and the use of remote sensing data is essential since the installation and maintenance of an extensive network of ground-based stations are impossible. Therefore, it is important to evaluate the applicability of satellites and sensors by comparing data with in-situ observations. In this work, we present the results from the analysis of land surface temperatures from Barton Peninsula, an ice-free area in King George Island (South Shetlands). We have studied the period from March 1, 2019 to January 31, 2020 using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and in-situ data from 6 ground temperature loggers. MOD11A1 and MYD11A1 products, from TERRA and AQUA satellites, respectively, were used, following the application of MODIS quality filters. Given the scarce number of high-quality data as defined by MODIS, all average LST with error &#8804; 2K were included. Dates with surface temperature below -20&#186;C, which are rare in the study area, and dates when the difference between MODIS and in-situ data exceeded 10&#186;C were also examined. In both cases, those days on which MOD09GA/MYD09GA products showed cloud cover were eliminated. Eight in-situ ground temperature measurements per day were available, from which the one nearest to the time of satellite overpass was selected for comparison with MODIS-LST. The results obtained show a better correlation with daytime data than with nighttime data. Specifically, the best results are obtained with daytime data from AQUA (R2 between 0.55 and 0.81). With daytime data, correlation between MODIS-LST and in-situ data was verified with relative humidity (RH) values provided by King Sejong weather station, located in the study area. When RH is lower, the correlation between LST and in-situ data improves: we obtained correlation coefficients between 0.6 - 0.7 for TERRA data and 0.8 - 0.9 for AQUA data with RH values lower than 80%. The results suggest that MODIS can be used for temperature estimation in the ice-free areas of the Maritime Antarctic.References:Bockheim, J. G., Campbell, I. B., Guglielmin, M., and L&#243;pez- Mart&#305;nez, J.: Distribution of permafrost types and buried ice in ice free areas of Antarctica, in: 9th International Conference on Permafrost, 28 June&#8211;3 July 2008, Proceedings, University of Alaska Press, Fairbanks, USA, 2008, 125&#8211;130.Vieira, G.; Bockheim, J.; Guglielmin, M.; Balks, M.; Abramov, A. A.; Boelhouwers, J.; Cannone, N.; Ganzert, L.; Gilichinsky, D. A.; Goryachkin, S.; L&#243;pez-Mart&#237;nez, J.; Meiklejohn, I.; Raffi, R.; Ramos, M.; Schaefer, C.; Serrano, E.; Simas, F.; Sletten, R.; Wagner, D. Thermal State of Permafrost and Active-layer Monitoring in the Antarctic: Advances During the International Polar Year 2007-2009. Permafr. Periglac. Process. 2010, 21, 182&#8211;197.&#160;AcknowledgementsThis work was made possible by an internship at the IGOT, University of Lisbon, Portugal, funded by the Principality of Asturias (code EB20-16).&#160;

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Evaluation of Seven Atmospheric Profiles from Reanalysis and Satellite-Derived Products: Implication for Single-Channel Land Surface Temperature Retrieval

Remote Sensing ◽

10.3390/rs12050791 ◽

2020 ◽

Vol 12 (5) ◽

pp. 791 ◽

Cited By ~ 6

Author(s):

Jingjing Yang ◽

Si-Bo Duan ◽

Xiaoyu Zhang ◽

Penghai Wu ◽

Cheng Huang ◽

...

Keyword(s):

Time Series ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Single Channel ◽

Landsat 8 ◽

Atmospheric Profiles ◽

Atmospheric Profile

Land surface temperature (LST) is vital for studies of hydrology, ecology, climatology, and environmental monitoring. The radiative-transfer-equation-based single-channel algorithm, in conjunction with the atmospheric profile, is regarded as the most suitable one with which to produce long-term time series LST products from Landsat thermal infrared (TIR) data. In this study, the performances of seven atmospheric profiles from different sources (the MODerate-resolution Imaging Spectroradiomete atmospheric profile product (MYD07), the Atmospheric Infrared Sounder atmospheric profile product (AIRS), the European Centre for Medium-range Weather Forecasts (ECMWF), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), the National Centers for Environmental Prediction (NCEP)/Global Forecasting System (GFS), NCEP/Final Operational Global Analysis (FNL), and NCEP/Department of Energy (DOE)) were comprehensively evaluated in the single-channel algorithm for LST retrieval from Landsat 8 TIR data. Results showed that when compared with the radio sounding profile downloaded from the University of Wyoming (UWYO), the worst accuracies of atmospheric parameters were obtained for the MYD07 profile. Furthermore, the root-mean-square error (RMSE) values (approximately 0.5 K) of the retrieved LST when using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were smaller than those but greater than 0.8 K when the MYD07, AIRS, and NCEP/DOE profiles were used. Compared with the in situ LST measurements that were collected at the Hailar, Urad Front Banner, and Wuhai sites, the RMSE values of the LST that were retrieved by using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were approximately 1.0 K. The largest discrepancy between the retrieved and in situ LST was obtained for the NCEP/DOE profile, with an RMSE value of approximately 1.5 K. The results reveal that the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles have great potential to perform accurate atmospheric correction and generate long-term time series LST products from Landsat TIR data by using a single-channel algorithm.

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